Anomaly Detection

Abstract

1. Introduction

This repository records how I explored several early tabular anomaly-detection methods with Python—especially PCA / Kernel PCA reconstruction error and RobustPCC. The historically preserved repository materials—code, formula notes, explanations, figures, and experiment records—are the main content of the project.

KADOA is presented here as an experimental variation proposed by Ma Xiao. It retains the broader clustering-and-weighting structure of ADOA while replacing the Isolation Forest component with Kernel PCA reconstruction error.

KADOA has not been presented as a peer-reviewed standalone method, and the historical repository experiment does not establish universal superiority over ADOA. If you use or discuss it, describe it as an experimental repository method, acknowledge its relationship to ADOA, and cite this repository.

2. Objective

UnSupervised and Semi-Supervise Anomaly Detection / IsolationForest / KernelPCA Detection / ADOA / etc.

This project demonstrates how Python can be applied to a real-world Cybersecurity & Ethical Hacking problem.

4. Technology Stack

• standardize the training data;
• use a Mahalanobis-style score to trim a small proportion of potential extremes;
• fit PCA on the remaining observations;
• identify major components that explain roughly the first 50% of variance;
• identify minor components whose eigenvalues fall below the historical threshold;
• calculate normalized deviations on both component groups;
• classify a sample as anomalous when either deviation exceeds its quantile threshold.
• major-component deviation: observations with extreme values in the original variables;

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Albertsr/Anomaly-Detection.git
cd Anomaly-Detection

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Add logging and alert notifications (email / Telegram)
• Write a threat model document for the tool
• Package it with Docker for safe lab testing

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. Which threat or attack does this project defend against?
2. What detection or protection technique is used and what are its limits?
3. How are false positives and false negatives handled?
4. Which cryptographic algorithms or security standards are involved?
5. What legal and ethical rules apply when testing a tool like this?

9. Source Code & License

This project is developed by Albertsr and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.